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AI Choice Engine

Mistral

Mistral Large 3

Mistral Large 3 is a Mistral model with 262K context, $0.50 in / $1.50 out per million tokens, last verified October 25, 2026. Open weights: yes. Public-board scores are linked to the publisher; vendor-only figures stay in the watch-out, not on the board.

Mistral's December 2025 multimodal 675B/41B sparse MoE with a 256K context; Medium 3.5 is a separate dense model with configurable reasoning.

Recorded profile verification October 25, 2026Individual provider fields may change

AI model specification details
SpecificationMistral Large 3
ProviderMistral
TierFrontier
Context window262K
Max outputNot verifiedUnverified
Input / 1M tokens$0.50
Output / 1M tokens$1.50
WeightsOpen
Parameters675B total / 41B active (sparse MoE)
Reasoning levelsNot verifiedUnverified
Modalitiestext, image
LicenseApache 2.0
API model idmistral-large-2512
ReleasedDecember 2, 2025

Pricing tiers: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific. These are the default Standard USD values in the retained pricing view. Batch/Priority and regional controls are offered, but their exact tariffs and eligibility are not established by that view; do not assume a universal discount or residence guarantee.

Publisher evidence

Published benchmark results

Each result keeps its source date visible. Dates refer to source publication or capture unless the source explicitly states when it measured the result. A missing benchmark is not treated as a zero.

Published benchmark results for Mistral Large 3
BenchmarkScoreSource dateSource
Artificial Analysis Intelligence IndexMistral Large 392026-10-01Artificial Analysis · View source
GPQA DiamondCross-model comparability not verified68%2026-09-26Artificial Analysis GPQA Diamond (independent; replaces Mistral's non-comparable base-model 5-shot 43.9) · View source

Best for

  • Open-weight deployments
  • Multimodal EU-hosted inference
  • Balanced frontier work

Watch out

The publisher describes Large 3 as not a dedicated reasoning model and documents vision limitations. It advertises FP8 deployment on a B200/H200 node or a separate NVFP4 checkpoint on H100/A100 nodes; this is not a guarantee for every serving setup. A separate hard output cap is not verified. Confirm regional residency on the actual endpoint.

Common questions

Mistral Large 3

Answered from the verified figures on this page rather than general guidance.

How much does Mistral Large 3 cost per million tokens?
Mistral Large 3 lists base rates of $0.50 per million input tokens and $1.50 per million output tokens. Estimate both input and output token volume and any additional charges for your workload. Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific. These are the default Standard USD values in the retained pricing view. Batch/Priority and regional controls are offered, but their exact tariffs and eligibility are not established by that view; do not assume a universal discount or residence guarantee.
What is Mistral Large 3's context window?
Mistral Large 3 accepts about 262K tokens of context. That only matters if you routinely send very long documents, large codebases, or multi-turn histories that approach that limit.
What is Mistral Large 3 best for?
Mistral Large 3 is a frontier tier from Mistral. It suits open-weight deployments, multimodal eu-hosted inference, balanced frontier work. The publisher describes Large 3 as not a dedicated reasoning model and documents vision limitations. It advertises FP8 deployment on a B200/H200 node or a separate NVFP4 checkpoint on H100/A100 nodes; this is not a guarantee for every serving setup. A separate hard output cap is not verified. Confirm regional residency on the actual endpoint.
Can I self-host Mistral Large 3?
Mistral Large 3 publishes open weights, but self-hosting depends on the licence, hardware footprint, quantisation quality, and serving stack. A hosted API is often cheaper until you have measured throughput and concurrency on your own hardware.